1. Field of the Invention
This invention relates to machine vision, and more particularly, to methods and apparatus for accurately locating curvilinear patterns in a digitized image.
2. Background
Many production machines require automated alignment of objects such as printed circuit boards before processing operations can take place. In such applications, machine vision systems can be used to capture an image of the object to be aligned and analyze that image for a match to a pretrained "model" of a fiducial or locator target on the object. Typically, normalized correlation search techniques are used in such vision systems to perform the image analysis against the model. The vision system can thus measure the x,y coordinates of a fiducial located on the object which best matches the pretrained model, and send those results to the production machine equipment, to move the physical object for proper alignment.
Angular fiducial symbols, such as squares, rectangles, crosses and triangles, can be found using a number of techniques that are both fast and accurate. Circular or curvilinear fiducials can also be found using existing techniques, including the use of searching for subfeatures of the object, such as arc segments situated at intervals around the circumference, as illustrated in FIG. 2. Subfeature models are trained from a generated full resolution synthetic image or model of the fiducial.
One of the problems, however, with circular or other curvilinear fiducials is that they do not have the distinct, scale independent corner or angle features that angular fiducials have. When an actual rectangular fiducial changes in scale, its four corners still look the same.
When an actual circular or curvilinear fiducial changes in scale, its "corners" or arc segments change in shape. Since submodels for circular or curvilinear fiducials generated according to existing methods do not change in shape, this leads to problems in correctly locating the fiducial. If scale variation is significant, it may not be possible to locate the fiducial with existing techniques.
Scale factor changes are often caused by variations in the manufacturing process. The degree to which these variations can be tolerated determines the success of the fiducial location apparatus.
One solution to problems of scale change involves designing a region of "leniency" around the edge of a subfeature model of the circular or curvilinear object. (FIG. 3). This area of leniency is usually represented to the machine vision system using pixel or pel values that correspond to a "don't care" zone around the outer edge of the submodel. Points found within this area may be considered matches. This solution sometimes provides better results in locating the object despite a change in scale, but it can also lead to inexactitude in the location of the object, thereby reducing accuracy and repeatability. In this approach, a search will find one of several good matches around the periphery of the curvilinear object for a particular submodel. As a result, while the leniency zone increases the chances that something will be found, the precise location of the fiducial or object may not be as accurately determined using this approach. This, in turn, can lead to inaccuracies in any alignment or positioning actions that are determined by the results of the search for the object.